Applied Deep Learning
Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You'll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function.
The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions.
Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to ...
Mastering Python for Networking and Security
It's becoming more and more apparent that security is a critical aspect of IT infrastructure. A data breach is a major security incident, usually carried out by just hacking a simple network line. Increasing your network's security helps step up your defenses against cyber attacks. Meanwhile, Python is being used for increasingly advanced tasks, with the latest update introducing many new packages. This book focuses on leveraging these updated packages to build a secure network with the help of Python scripting.
This book covers topics from building a network to the different procedures you need to follow to secure it. You'll first be introduced to different packages and libraries, before moving on to different ways to build a network with the help of Python scripting. Later, you will learn how to check a network's vulnerability using Python security scripting, and understand how to check vulnerabilities in your network. As you progress through the chapte ...
Learning OpenStack Networking, 3rd Edition
OpenStack Networking is a pluggable, scalable, and API-driven system to manage physical and virtual networking resources in an OpenStack-based cloud. Like other core OpenStack components, OpenStack Networking can be used by administrators and users to increase the value and maximize the use of existing datacenter resources. This third edition of Learning OpenStack Networking walks you through the installation of OpenStack and provides you with a foundation that can be used to build a scalable and production-ready OpenStack cloud.
In the initial chapters, you will review the physical network requirements and architectures necessary for an OpenStack environment that provide core cloud functionality. Then, you'll move through the installation of the new release of OpenStack using packages from the Ubuntu repository. An overview of Neutron networking foundational concepts, including networks, subnets, and ports will segue into advanced topics such as security groups, distributed ...
Natural Language Processing with Java, 2nd Edition
Natural Language Processing (NLP) allows you to take any sentence and identify patterns, special names, company names, and more. The second edition of Natural Language Processing with Java teaches you how to perform language analysis with the help of Java libraries, while constantly gaining insights from the outcomes.
You'll start by understanding how NLP and its various concepts work. Having got to grips with the basics, you'll explore important tools and libraries in Java for NLP, such as CoreNLP, OpenNLP, Neuroph, and Mallet. You'll then start performing NLP on different inputs and tasks, such as tokenization, model training, parts-of-speech and parsing trees. You'll learn about statistical machine translation, summarization, dialog systems, complex searches, supervised and unsupervised NLP, and more.
By the end of this book, you'll have learned more about NLP, neural networks, and various other trained models in Java for enhancing the performance of NLP applications. ...
Hands-On Convolutional Neural Networks with TensorFlow
Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time!
We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation.
After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks.
Later on, you will see useful tips ...
Hands-On Transfer Learning with Python
Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.
The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples.
The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with pract ...
Deep Learning for Natural Language Processing
Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models.
You'll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system.
This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be availa ...
Raspberry Pi for Arduino Users
Leverage your Arduino skills in the Raspberry Pi world and see how to cross the two platforms into sophisticated programs.
The Arduino and Raspberry Pi communities overlap more than you might think. Arduinos can be expanded to have network capabilities with a variety of "shields", all of which increase the cost and complexity of the system. By contrast, Raspberry Pis all run Linux, which is a very network-competent platform. The newest Pi, the Raspberry Pi Zero W, is WiFi and Bluetooth capable, and costs around $10 U.S. For network enabled gadgets, it makes far more sense to cross to the Raspberry PI platform, if only someone would make it easy to do. That's what this book is about.
You'll learn some survival level Linux system administration, so you know how to set the machine up and how to establish at least minimal security for your gadget. You'll set up and learn the Geany IDE on your Pi, which is fairly similar to the Arduino IDE.
Where the two platforms ov ...
Explore how to develop and implement wireless server networks (WSN) using Contiki-NG, branded as the operating system for the IoT. The book explains Contiki-NG's advantages in sensing, communication, and energy optimization and enables you to begin solving problems in automation with WSN.
Practical Contiki-NG is a guide to getting started with Contiki-NG programming featuring projects that demonstrate a variety of applications. This book takes a practical and content-driven approach to the latest technologies, including Raspberry Pi, IoT and cloud servers. Readers will go through step-by-step guides and sample scenarios such as sensing, actuating, connectivity, building middleware, and utilizing IoT and cloud-based technologies.
If you're looking to go from zero to hero in using Contiki-NG to build Wireless Sensor Network (WSN) applications then this is the book for you.
Prepare and set up Contiki-NG development; Review the basics of the Contiki-NG platform to build Wir ...
Practical Network Scanning
Network scanning is the process of assessing a network to identify an active host network; same methods can be used by an attacker or network administrator for security assessment. This procedure plays a vital role in risk assessment programs or while preparing a security plan for your organization.
Practical Network Scanning starts with the concept of network scanning and how organizations can benefit from it. Then, going forward, we delve into the different scanning steps, such as service detection, firewall detection, TCP/IP port detection, and OS detection. We also implement these concepts using a few of the most prominent tools on the market, such as Nessus and Nmap. In the concluding chapters, we prepare a complete vulnerability assessment plan for your organization.
By the end of this book, you will have hands-on experience in performing network scanning using different tools and in choosing the best tools for your system. ...
Networking All-in-One For Dummies, 7th Edition
Whether you're in charge of a small network or a large network Networking All-in-One is full of the information you'll need to set up a network and keep it functioning. Fully updated to capture the latest Windows 10 releases through Spring 2018, this is the comprehensive guide to setting up, managing, and securing a successful network.
Inside, nine minibooks cover essential, up-to-date information for networking in systems such as Windows 10 and Linux, as well as best practices for security, mobile and cloud-based networking, and much more.
Serves as a single source for the most-often needed network administration information; Covers the latest trends in networking; Get nine detailed and easy-to-understand networking minibooks in one affordable package.
Networking All-in-One For Dummies is the perfect beginner's guide as well as the professional's ideal reference book. ...